pamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic and light data in R

Dhanjal‐Adams, Kiran L.; Willener, Astrid S. T.; Liechti, Félix

doi:10.1101/2021.08.02.454456

Cited by 4 publications

(6 citation statements)

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“…The sensors measured light intensity and acceleration every 5 min and air pressure every 30 min. The logger compressed the raw accelerometer data (Liechti et al, 2018), and we then classified them into low and high activity with a threshold method in the R‐software package PAMLr (https://github.com/KiranLDA/PAMLr; Dhanjal‐Adams et al, 2020). The light‐level geolocation model provides two position estimates per day (Lisovski et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…We analysed data from European Hoopoes Upupa epops equipped with multi-sensor loggers, which recorded year-round (a) light-level intensities to estimate positions (Lisovski et al, 2020), (b) acceleration data to estimate flight times (Dhanjal-Adams et al, 2020) and (c) barometric pressure to estimate the flight altitude (Liechti et al, 2018). In addition we, retrieved wind data (4) from the ERA5 wind database (Berrisford et al, 2011).…”

Section: Datamentioning

confidence: 99%

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Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

2022

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To investigate the complex phenomenon of bird migration, researchers rely on sophisticated methods for tracking long‐distant migrants. While large birds can be equipped with satellite tags, these are too heavy for many species. Instead, researchers often use light‐level geolocation for tracking individual small migratory birds. Unfortunately, light‐level geolocation is often coarse and unreliable, with positioning errors of anything up to hundreds of kilometres. Recent Bayesian models try to constrain the route to plausible corridors: they couple light‐level measurements with information about the bird's likely movement. While these models improve inference, they still lack information on weather conditions, specifically the impact of wind. For example, birds might encounter tailwinds—considerably increasing their (ground) speed and making longer routes more likely, or headwinds—having the opposite effect. Miniaturised multi‐sensor tags allow monitoring not only light but also acceleration and air pressure. These measurements provide essential additional information about the exact timing of flight activity and the corresponding flight altitudes. This article proposes a Bayesian model for inferring bird migration. The model integrates air pressure to estimate flight altitudes and considers wind data to calculate the most likely flight trajectory. The model constrains the migratory routes to those likely given by the winds en route and the observed timing of flight activity. We apply the model to infer the migration of European Hoopoes Upupa epops. Adding wind data for route inference excludes flight trajectories with unrealistic high airspeeds, decreases the uncertainty of the position estimates and returns more plausible migratory routes. Faithful reconstruction of migratory routes helps unravel the influence of physiological and environmental factors on bird migration. This is crucial for habitat protection where limited resources need to be allocated to relevant areas.

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Section: Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

2022

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“…Data available from Zenodo repository https://doi.org/10.5281/ zenodo.6327701 (Dhanjal-Adams et al, 2022) containing the code from the manuscript, a copy of the R environment and the R package. The package manual is also available and updated through this link https://kiran lda.github.io/Pamlr Manua l/index.html.…”

Section: Ack N Owled G Em Entsmentioning

confidence: 99%

pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Dhanjal‐Adams

Willener

Liechti

2022

Journal of Animal Ecology

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Light‐level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large‐scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k‐means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events). Finally, we highlight some of the limitations, implications and opportunities of using these methods.

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“…Because the pressure analysis requires high precision of classification, we manually label the activity and pressure data using TRAINSET (Kapoor et al, 2021). To Global positioning with pressure sensors accelerate the process, we initialise the labialization with an automatic classification of activity following Dhanjal-Adams et al (2021). Detailed guidelines, examples and tips on this labelling process can be found at https://raphaelnussbaumer.com/GeoPressureR/articles/labelling-tracks.…”

Section: Identification Of Stationary Periodsmentioning

confidence: 99%

“…More recently, multi-sensor geolocators have expanded the field of research by providing information on movement (accelerometer), temperature, and pressure, in addition to light (Liechti et al, 2018). Accelerometer data can be used to determine when and how long a bird was stationary or in movement (Dhanjal-Adams et al, 2021). This information helps to improve the position of the geolocator in two ways: (1) all twilights occurring during the known Global positioning with pressure sensors stationary periods can be combined to estimate a single location and (2) assuming a certain ground speed, the duration of flight can be converted into a distance, ultimately constraining consecutive locations of the bird (e.g., SGAT; Wotherspoon et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Global positioning with animal-borne pressure sensors

Nussbaumer

Gravey

Briedis

et al. 2022

Preprint

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BackgroundUnderstanding the temporal and spatial use of habitats by wildlife is crucial to apprehend ecological relationships in nature. Tracking small birds and bats requires tags of less than 2g, therefore lightweight geolocators are currently the most affordable and widespread option. Recent multi-sensor geolocators now capture accelerometer and pressure data in addition to light, offering new potential to refine the accuracy of bird positioning. In particular, as atmospheric pressure varies with space and time, pressure timeseries at a single location have a unique signature which can be used for global positioning independent of light recordings.MethodBird positions are estimated at distinct stationary periods by comparing atmospheric pressure data recorded by the geolocator with reference data from a global weather reanalysis database. The methodology produces a probability distribution map of the position based on (1) a mask of the locations for which the ground level elevation matches the pressure measured by the geolocator and (2) a likelihood of the mismatch between the temporal timeseries measured by the geolocator and the reanalysis dataset. This new method is introduced step by step and applied to 16 tracks from 9 long- and short-distance migrant species.ResultsUsing known positions of double-tagged individuals (light and pressure data), we demonstrate that our method is almost three times more accurate than light-based positioning with an average error of 44 km in our trials. In contrast to the traditional light-based approach, pressure data can provide location information for short stationary periods (less than a day) and is not affected by the equinox-problem nor the change of habitats by the tracked animal. To facilitate the application of the method, we developed an R package featuring guidelines, examples and starting code (GeoPressureR).ConclusionPositioning birds using pressure data can help identify short stopover locations, such that tracking devices measuring pressure alone could be used for global positioning, allowing for the study of lighter species.

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pamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic and light data in R

Cited by 4 publications

References 33 publications

Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Global positioning with animal-borne pressure sensors

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